Comparative Study of Rule-Based and Machine Learning Approaches in Intrusion Detection: A Case Study of University of Abuja (Gwagwalada LGA, FCT Abuja)
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Background of the Study
With the rapid expansion of digital services in educational institutions, the risk of cyber-attacks, including unauthorized access and data breaches, has become a significant concern. The University of Abuja, located in Gwagwalada LGA, FCT Abuja, is no exception. Intrusion Detection Systems (IDS) are essential in identifying and responding to potential threats to university networks and data. Traditionally, IDS have relied on rule-based systems that use predefined rules to detect suspicious activities. However, the increasing sophistication of cyber-attacks has led to the development of Machine Learning (ML) approaches, which allow IDS to learn from patterns in network traffic and adapt to new, previously unseen threats. This study aims to compare the performance of rule-based IDS and ML-based IDS in detecting network intrusions at the University of Abuja, focusing on detection accuracy, false positive rates, and response times.
Statement of the Problem
The University of Abuja has faced several security breaches over the years, highlighting weaknesses in the existing intrusion detection system. The rule-based IDS currently in place relies on predefined patterns and often struggles to identify novel threats. Furthermore, these systems can produce a high number of false positives, leading to unnecessary alerts and inefficiencies. The university requires a more dynamic and intelligent approach to intrusion detection, which can adapt to evolving threats. Although machine learning-based IDS have shown promise, there is limited research on their effectiveness in comparison to rule-based systems in the context of university networks.
Objectives of the Study
- To compare the performance of rule-based and machine learning-based intrusion detection systems at the University of Abuja.
- To evaluate the accuracy, false positive rates, and response times of both IDS approaches in detecting intrusions.
- To recommend the most effective IDS approach for securing the university’s network infrastructure.
Research Questions
- How do rule-based and machine learning-based intrusion detection systems compare in detecting intrusions at the University of Abuja?
- What are the advantages and disadvantages of rule-based and machine learning-based approaches in terms of accuracy and false positives?
- How do rule-based and machine learning-based IDS impact the efficiency of network security management at the University of Abuja?
Research Hypotheses
- Machine learning-based IDS will have a higher detection accuracy and lower false positive rate compared to rule-based IDS at the University of Abuja.
- Machine learning-based IDS will outperform rule-based IDS in terms of response time for detecting and mitigating network intrusions.
- The use of machine learning-based IDS will improve the overall efficiency and effectiveness of the University of Abuja’s network security management.
Significance of the Study
This study will provide valuable insights into the application of machine learning techniques for intrusion detection in university networks. By comparing rule-based and machine learning-based approaches, the study will guide the University of Abuja in selecting the most effective IDS for protecting its network infrastructure. The findings will also contribute to the broader body of knowledge on IDS in educational institutions, with implications for other universities facing similar security challenges.
Scope and Limitations of the Study
The study will focus on the comparison of rule-based and machine learning-based intrusion detection systems at the University of Abuja, located in Gwagwalada LGA, FCT Abuja. The study will specifically evaluate the detection accuracy, false positives, and response times of both approaches in the university’s network. The study will not address other aspects of network security, such as firewalls or encryption systems.
Definitions of Terms
Intrusion Detection System (IDS): A security system designed to detect unauthorized access or attacks on a network or computer system.
Rule-Based IDS: An IDS that uses predefined rules and signatures to detect intrusions.
Machine Learning-Based IDS: An IDS that uses machine learning algorithms to analyze network traffic and detect anomalies or potential intrusions.
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